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Const-me
Whisper
C++2023–2024
Const-me / Whisper

Whisper

High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model

AI & Machine Learning
Explore on GitHubRead Hacker News discussions
7Distinct people
30Mentions
0Explicit recommenders
10.7KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:04:58 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month30 dated mentions

Observed mentions: 2023-01-16 15:43:29 UTC to 2024-08-12 20:29:22 UTC.

02468102023-01-01 00:00:00 UTC to 2023-02-01 00:00:00 UTC (exclusive): 9 mentions; 1 submissions, 8 commentsJan 20232023-02-01 00:00:00 UTC to 2023-03-01 00:00:00 UTC (exclusive): 7 mentions; 0 submissions, 7 comments2023-03-01 00:00:00 UTC to 2023-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-04-01 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-01 00:00:00 UTC to 2023-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-06-01 00:00:00 UTC to 2023-07-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2023-07-01 00:00:00 UTC to 2023-08-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2023-08-01 00:00:00 UTC to 2023-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-10-01 00:00:00 UTC to 2023-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 20232023-11-01 00:00:00 UTC to 2023-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jan 20232023-01-01 00:00:00 UTC2023-02-01 00:00:00 UTC918
Feb 20232023-02-01 00:00:00 UTC2023-03-01 00:00:00 UTC707
Mar 20232023-03-01 00:00:00 UTC2023-04-01 00:00:00 UTC101
Apr 20232023-04-01 00:00:00 UTC2023-05-01 00:00:00 UTC000
May 20232023-05-01 00:00:00 UTC2023-06-01 00:00:00 UTC101
Jun 20232023-06-01 00:00:00 UTC2023-07-01 00:00:00 UTC110
Jul 20232023-07-01 00:00:00 UTC2023-08-01 00:00:00 UTC303
Aug 20232023-08-01 00:00:00 UTC2023-09-01 00:00:00 UTC000
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC101
Oct 20232023-10-01 00:00:00 UTC2023-11-01 00:00:00 UTC000
Nov 20232023-11-01 00:00:00 UTC2023-12-01 00:00:00 UTC000
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC303
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC000
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC101
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC101
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC101
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC000
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC101

What Hacker News says

3 sources
Const-me·Jul 26, 2023
> driver bugs linked to OpenCL and ROCm CUDA is the default tech for GPGPU in HPC or AI applications, for more than a decade now. By now, people have found most of these driver bugs, and nVidia has fixed them. Similarly, compute shaders is the only tech for GPGPU used in videogames. Modern videogames are using compute shaders for a decade now, in increasing volumes. For example, UE5 even renders triangle meshes with them [1]. However, OpenCL and ROCm are niche technologies. I’ve been hearing complaints about driver quality for some time now. For obvious reason, AMD and Intel prioritize driver bugs which affect modern videogames sold in many million copies, compared to the bugs which only affect a few people working on HPC, AI or other niche GPGPU applications. > they're underestimating the power of a few good public "how to use the damn t
Read the rest
hing" sessions I agree the learning curve is steep, with the lack of good materials. For an introduction article, see [2]. Ignore the parts about D3D10 hardware, the article is old and D3D10 hardware is no longer relevant. Another one, with slightly more depth, is [3]. For an example how to multiply large dense matrices with a compute shader see [4], but that example is rather advanced because optimizations, and because weird memory layout conventions inherited from the upstream project. [1] https://www.youtube.com/watch?v=TMorJX3Nj6U [2] https://developer.download.nvidia.com/compute/DevZone/docs/h... [3] https://github.com/jstoecker/dxcompute-docs/tree/main [4] https://github.com/Const-me/Whisper/blob/master/ComputeShade...
In Which GPU(s) to Get for Deep Learning
Const-me·Jul 7, 2023
Gamers don’t care about FP64 performance, and it seems nVidia is using that for market segmentation. The FP64 performance for RTX 4090 is 1.142 TFlops, for RTX 3090 Ti 0.524 TFlops. AMD doesn’t do that, FP64 performance is consistently better there, and have been this way for quite a few years. For example, the figure for 3090 Ti (a $2000 card from 2022) is similar to Radeon Vega 56, a $400 card from 2017 which can do 0.518 TFlops. And another thing: nVidia forbids usage of GeForce cards in data centers, while AMD allows that. I don’t know how specifically they define datacenter, whether it’s enforceable, or whether it’s tested in courts of various jurisdictions. I just don’t want to find out answers to these questions at the legal expenses of my employer. I believe they would prefer to not cut corners like that. I think nVidia only beats
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AMD due to the ecosystem: for GPGPU that’s CUDA (and especially the included first-party libraries like BLAS, FFT, DNN and others), also due to the support in popular libraries like TensorFlow. However, it’s not that hard to ignore the ecosystem, and instead write some compute shaders in HLSL. Here’s a non-trivial open-source project unrelated to CAE, where I managed to do just that with decent results: https://github.com/Const-me/Whisper That software even works on Linux, probably due to Valve’s work on DXVK 2.0 (a compatibility layer which implements D3D11 on top of Vulkan).
In Why Nvidia Keeps Winning

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